1. Field of the Invention
The present invention relates generally to electronic devices, and more specifically, to a method and device for transliteration in an electronic device.
2. Description of the Related Art
Most electronic devices include an input module that is configured to generate input in a particular language. For example, a mobile phone can include keypad that is configured to generate input only in the English language. Users desiring to provide text input to the electronic device in their native language can frequently use these electronic devices. In such a scenario, a user may not be able to use these electronic devices to conveniently input text in their native language. For example, a user desires to send a Short Messaging Service (SMS) message in Korean language but may not be able to do so, due to unavailability of a keypad configured to accept text in the Korean language.
For a user to enter a text in a script/language different from the script/language for which the keypad is designed to provide input to the electronic device, transliteration is the only option. Transliteration can broadly be defined as a process of entering data in one language, hereafter referred to as source language, by using the script of another language, hereafter referred to as target language.
In one known technique of transliteration, static mapping of a source language character with a target language character is stored in the electronic device, resulting in excessive memory use. In this technique a user is required to remember a keystroke in the source language that corresponds to the desired character in the target language. In addition, in this technique transliteration of words is case sensitive. For example, ‘d’ on an English keypad can be used for typing for  (in Hindi language) and ‘D’ can be used for typing  (in Hindi language). However, users generally use ‘d’ to type both  and  As result, a user has to follow a complex syntax to construct output and the electronic device requires additional processing power.
According to another known technique, a decision tree is created based on a position of each source language character in source words by using an automated learning model. The decision tree is composed of a number of rules that describe a mapping of a particular character in a target language to be programmatically mapped to various possible ways of transliterating the character in a source language, based on the context of that character. The context can be defined by 4 to 5 preceding and succeeding source and target language characters. As a result, the decision tree predicts the target language character that should appear for corresponding source language character, depending on the context of the character in a word and by selecting the appropriate rule.
The learning model described in the foregoing technique is based on raw learning of each source language character, at each position of occurrence of the character in a word. The learning for each character of a source language creates a number of possible mappings to that of target language characters based on the context of source language character. As there can be multiple occurrences of a source language character in a word, generating the rules for each character based on its context at each position of its occurrence requires a large amount of memory. Further, the training model for target language is dependent on the source language. This requires the training model (for a particular target language) to be executed for each instance of the source language.
In yet another technique, transliteration is based on the bilingual dictionary of segments corresponding to a word-pair. Each word-pair includes a source word to which a large number of target words correspond. Each source word is specified in a source language and each target word is a transliteration of a corresponding source word in a target language. The word-pairs are ranked based on the statistical information from a corpus of parallel text having first text in a source language and second text in a target language. The decision of which target language segment should appear for a particular source language segment is based on the ranks being assigned to each word pair. To further enhance the transliteration, the lookup for the transliteration of word is performed in a bilingual dictionary in which each word in source language has a corresponding transliterated word in the target language.
The learning model described in the previous technique employs use of a bilingual dictionary to store a number of segments and words in the form of a word-pair where a source language word (or segment) is directly mapped to its corresponding target language word (or segment). As a result, storing information for bilingual entries in a dictionary requires the use of a large amount of memory. Further employing a bilingual dictionary involves dependency of a source language over a target language and vice-versa.